Ethical and Trustworthy Artificial Intelligence: Theoretical approaches and Practical applications

Goal

Upon successful completion of the course, the students will be able to:

• Understand the importance of ethical and reliable Artificial Intelligence (AI), the ethical challenges and ethical questions raised when developing an AI system.
• Understand key concepts that describe reliable AI and how these can be put into practice during the development of AI methods
• Understand the difficulties that the interaction of humans with AI imposes on the development of AI algorithms
• Get a complete picture of the regulatory framework concerning or related to IT through a detailed legal taxonomy
• Know and apply evaluation methods for the compliance of a system with the principles of ethical and reliable AI.

Also, the course targets to the following general competencies:

• Ability to organize and plan work and time management
• Ability to communicate effectively (orallyl and written)
• Ability to solve problems
• Ability to develop critical thinking and capacity for critical approaches
• Ability to work in a team
• Ability of interdisciplinary approaches
• Ability to apply theoretical knowledge in practice
• Ability to research
• Exercise criticism and self-criticism

Contents

• Introduction to AI ethics; Definition of ethics, Working context for developing ethical AI
• Defining moral values: Experiential workshop based on the methodology developed by the EU research project VAST (link is external)
• Data Governance
• AI systems and the Alignment Problem
• Legal framework; A description of the legal taxonomy: GDPR, AIAct, Data Governance Act.
• Impact and Risk assessment; Methods and tools
• AI ethics prototypes
• Moral dilemmas in complex systems; description of moral theories.
• Use case 1: Application of AI impact assessment tools
• Use case 2: Application of AI impact assessment tools, case studies, example problems, and methods for solving them, etc., are presented

Bibliography

Books

• Shosana Zuboff. 2019. The Age of Surveillance Capitalism: The Fight for a human future at the new Frontier of Power.
• Jaron Lanier. 2014. Who Owns the Future.
• Kat Holmes. 2019. Mismatch, How Inclusion Shapes Design, MIT Press.
• Cathy O’Neil. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
• Brian Christian. 2020. The Alignment Problem.

Papers

• Luciano Floridi & Mariarosaria Taddeo. 2016. What is Data Ethics?, Oxford Internet Institute.
• Luciano Floridi. 2018. Soft Ethics and the Governance of the Digital, Royal Society in Philosophical Transactions of the Royal Society A”.
• Whitepaper - Emerging models of data governance in the age of datafication
• MyData Global. 2016. A Nordic Model for human centered personal data management and processing
• Whitepaper - Understanding MyData Operators
• IEEE Ethically Aligned Design v.2, A Vision for Prioritising Human Well Being with A/IS

Legal Drafts

• A European Strategy for Data (2020).
• Proposal for a Regulation on European Data Governance.
• Data Governance Q&A.
• The Digital Services Act package.